All of these augmentations are applied on-the-fly to every speech sentence processed by the neural network. This way, we generate a different contaminated data at every epoch.
In standard augmentation pipelines, the signal is contaminated with one or more augmentation strategies and then used for training a neural network. Instead, in this work, we propose to concatenate the original speech signal with all the contaminated versions produced by the aforementioned contamination techniques [1]}.
This way, within each training batch, our model observes the same sentence corrupted in six different ways. These different “views” of the same signal force the gradient to point to a direction of the parameter space that is inherently robust against signal variations, thus proving an important regularization effect.
